Detecting and responding to clusters of rapid HIV transmission is a core HIV prevention strategy in the United States, guiding public health interventions and identifying gaps in prevention and care services. In 2016, the Centers for Disease Control and Prevention (CDC) initiated molecular cluster detection using data from 27 jurisdictions. During 2016-2023, CDC expanded sequence reporting nationwide and deployed Secure HIV-TRACE, an application supporting health department (HD) molecular cluster detection. CDC conducts molecular cluster detection quarterly; state and local HDs analyze local data monthly. HDs began routinely reporting clusters to CDC by using cluster report forms in 2020. During 2018-2023, CDC identified 404 molecular clusters of rapid HIV transmission; 325 (80%) involved multiple jurisdictions. During 2020-2023, HDs reported 298 molecular clusters to CDC; 249 were first detected by HDs. Expanding molecular cluster detection has provided a foundation for improving service delivery to networks experiencing rapid HIV transmission.

INTRODUCTION: Optimal use of HIV testing resources accelerates progress towards ending HIV as a global threat. In Kenya, current testing practices yield a 2.8% positivity rate for new diagnoses reported through the national HIV electronic medical record (EMR) system. Increasingly, researchers have explored the potential for machine learning to improve the identification of people with undiagnosed HIV for referral for HIV testing. However, few studies have used routinely collected programme data as the basis for implementing a real-time clinical decision support system to improve HIV screening. In this study, we applied machine learning to routine programme data from Kenya's EMR to predict the probability that an individual seeking care is undiagnosed HIV positive and should be prioritized for testing. METHODS: We combined de-identified individual-level EMR data from 167,509 individuals without a previous HIV diagnosis who were tested between June and November 2022. We included demographics, clinical histories and HIV-relevant behavioural practices with open-source data that describes population-level behavioural practices as other variables in the model. We used multiple imputations to address high rates of missing data, selecting the optimal technique based on out-of-sample error. We generated a stratified 60-20-20 train-validate-test split to assess model generalizability. We trained four machine learning algorithms including logistic regression, Random Forest, AdaBoost and XGBoost. Models were evaluated using Area Under the Precision-Recall Curve (AUCPR), a metric that is well-suited to cases of class imbalance such as this, in which there are far more negative test results than positive. RESULTS: All model types demonstrated predictive performance on the test set with AUCPR that exceeded the current positivity rate. XGBoost generated the greatest AUCPR, 10.5 times greater than the rate of positive test results. CONCLUSIONS: Our study demonstrated that machine learning applied to routine HIV testing data may be used as a clinical decision support tool to refer persons for HIV testing. The resulting model could be integrated in the screening form of an EMR and used as a real-time decision support tool to inform testing decisions. Although issues of data quality and missing data remained, these challenges could be addressed using sound data preparation techniques.

IMPORTANCE: Invasive group A Streptococcus (GAS) infections are associated with substantial morbidity, mortality, and economic burden. OBJECTIVE: To update trends in invasive GAS disease incidence rates in 10 US states between 2013 and 2022. DESIGN, SETTING, AND PARTICIPANTS: Clinical, demographic, and laboratory data for invasive GAS cases were collected as part of population-based surveillance in the Active Bacterial Core surveillance network covering 34.9 million persons across 10 US states. A case was defined as isolation of GAS from a normally sterile site or from a wound in a patient with necrotizing fasciitis or streptococcal toxic shock syndrome between January 1, 2013, and December 31, 2022. Demographic and clinical data were collected from medical record review. From 2013 to 2014, available isolates were emm typed and antimicrobial susceptibilities determined using conventional methods; from 2015 onward, whole-genome sequencing was used. MAIN OUTCOMES AND MEASURES: Incidence rates by sex, age, race, and selected risk factors; clinical syndromes, outcomes, and underlying patient conditions; and isolate characteristics, including antimicrobial susceptibility. RESULTS: Surveillance in 10 US states identified 21 312 cases of invasive GAS from 2013 through 2022, including 1981 deaths. The majority of cases (57.5%) were in males. Among case-patients, 1272 (6.0%) were aged 0 to 17 years, 13 565 (63.7%) were aged 18 to 64 years, and 6474 (30.4%) were 65 years or older; 5.5% were American Indian or Alaska Native, 14.3% were Black, and 67.1% were White. Incidence rose from 3.6 per 100 000 persons in 2013 to 8.2 per 100 000 persons in 2022 (P < .001 for trend). Incidence was highest among persons 65 years or older; however, the relative increase over time was greatest among adults aged 18 to 64 years (3.2 to 8.7 per 100 000 persons). Incidence was higher among American Indian or Alaska Native persons than in other racial and ethnic groups. People experiencing homelessness, people who inject drugs, and residents of long-term care facilities had substantially elevated GAS incidence rates. Among tested isolates, those nonsusceptible to macrolides and clindamycin increased from 12.7% in 2013 to 33.1% in 2022. CONCLUSIONS: Invasive GAS infections increased substantially in 10 US states during a surveillance period from 2013 to 2022. Accelerated efforts to prevent and control GAS are needed, especially among groups at highest risk of infection.

In February 2023, the government of Equatorial Guinea declared an outbreak of Marburg virus disease. We describe the response structure and epidemiologic characteristics, including case-patient demographics, clinical manifestations, risk factors, and the serial interval and timing of symptom onset, treatment seeking, and recovery or death. We identified 16 laboratory-confirmed and 23 probable cases of Marburg virus disease in 5 districts and noted several unlinked chains of transmission and a case-fatality ratio of 90% (35/39 cases). Transmission was concentrated in family clusters and healthcare settings. The median serial interval was 18.5 days; most transmission occurred during late-stage disease. Rapid isolation of symptomatic case-patients is critical in preventing transmission and improving patient outcomes; community engagement and surveillance strengthening should be prioritized in emerging outbreaks. Further analysis of this outbreak and a One Health surveillance approach can help prevent and prepare for future potential spillover events.

OBJECTIVES: After respiratory syncytial virus (RSV), human metapneumovirus (hMPV) was the second-ranked pathogen attributed to severe pneumonia in the PERCH study. We sought to characterize hMPV-positive cases in high-burden settings, which have limited data, by comparing with RSV-positive and other cases. METHODS: Children aged 1-59 months hospitalized with suspected severe pneumonia and age/season-matched community controls in seven African and Asian countries had nasopharyngeal/oropharyngeal swabs tested by multiplex PCR for 32 respiratory pathogens, among other clinical and lab assessments at admission. Odds ratios adjusted for age and site (adjusted OR [aOR]) were calculated using logistic regression. Aetiologic probability was estimated using Bayesian nested partial latent class analysis. Latent class analysis identified syndromic constellations of clinical characteristics. RESULTS: hMPV was detected more frequently among cases (267/3887, 6.9%) than controls (115/4976, 2.3%), among cases with pneumonia chest X-ray findings (8.5%) than without (5.5%), and among controls with respiratory tract illness (3.8%) than without (1.8%; all p ≤ 0.001). HMPV-positive cases were negatively associated with the detection of other viruses (aOR, 0.18), especially RSV (aOR, 0.11; all p < 0.0001), and positively associated with the detection of bacteria (aORs, 1.77; p 0.03). No single clinical syndrome distinguished hMPV-positive from other cases. Among hMPV-positive cases, 65.2% were aged <1 year and 27.5% had pneumonia danger signs; positive predictive value for hMPV aetiology was 74.5%; mortality was 3.9%, similar to RSV-positive (2.4%) and lower than that among other cases (9.6%). DISCUSSION: HMPV-associated severe paediatric pneumonia in high-burden settings was predominantly in young infants and clinically indistinguishable from RSV. HMPV-positives had low case fatality, similar to that in RSV-positives.

OBJECTIVE: To estimate the influence of bursts of rapid HIV transmission on future transmission and describe populations affected by transmission bursts. DESIGN: Phylogenetic analysis of US National HIV Surveillance System data. METHODS: Time-scaled phylogenetic trees were inferred for six geographic regions using sequences from persons with HIV (PWH) with diagnoses of HIV infection 2014-2019. Transmission bursts were defined as ≥3 adjacent inferred transmission events in the phylogeny during a detection period. We calculated the relative contribution of transmission bursts 2015-2016 to transmission 2017-2019 compared with non-bursts. Then, we detected bursts within any sliding 2-year period 2014-2019 and assessed descriptive associations of characteristics of individuals involved with or descended from transmission bursts using univariate risk ratios. RESULTS: The 5.6% of phylogenetic lineages involved in transmission bursts 2015-2016 contributed to 14.9% of inferred transmission events 2017-2019. The relative contribution of lineages involved in transmission bursts to future transmission was 2.94 times that of lineages not involved in bursts. Younger age at diagnosis, self-identification as transgender or an additional gender identity, or as a cisgender man, male-to-male sexual contact, injection drug use, or male-to-male sexual contact and injection drug use, and diagnosis during acute or early infection were most strongly associated with involvement in or descendance from transmission bursts. CONCLUSIONS: Transmission bursts contribute disproportionately to future HIV transmission, underscoring the value of detecting and responding to rapid transmission to reduce incidence. Bursts of rapid transmission may also contribute to enduring disparities in incidence among some key populations.

BACKGROUND: Serology (the detection of antibodies formed by the host against an infecting pathogen) is frequently used to assess current infections and past exposure to specific pathogens. However, the presence of cross-reactivity among host antibodies in serological data makes it challenging to interpret the patterns and draw reliable conclusions about the infecting pathogen or strain. METHODOLOGY/PRINCIPAL FINDINGS: In our study, we use microscopic agglutination test (MAT) serological data from three host species [California sea lion (Zalophus californianus), island fox (Urocyon littoralis), and island spotted skunk (Spilogale gracilis)] with confirmed infections to assess differences in cross-reactivity by host species and diagnostic laboratory. All host species are known to be infected with the same serovar of Leptospira interrogans. We find that absolute and relative antibody titer magnitudes vary systematically across host species and diagnostic laboratories. Despite being infected by the same Leptospira serovar, three host species exhibit different cross-reactivity profiles to a 5-serovar diagnostic panel. We also observe that the cross-reactive antibody titer against a non-infecting serovar can remain detectable after the antibody titer against the infecting serovar declines below detectable levels. CONCLUSIONS/SIGNIFICANCE: Cross-reactivity in serological data makes interpretation difficult and can lead to common pitfalls. Our results show that the highest antibody titer is not a reliable indicator of infecting serovar and highlight an intriguing role of host species in shaping reactivity patterns. On the other side, seronegativity against a given serovar does not rule out that serovar as the cause of infection. We show that titer magnitudes can be influenced by both host species and diagnostic laboratory, indicating that efforts to interpret absolute titers (e.g., as indicators of recent infection) must be calibrated to the system under study. Thus, we implore scientists and health officials using serological data for surveillance to interpret the data with caution.

Beginning in late 2023, Oropouche virus was identified as the cause of large outbreaks in Amazon regions with known endemic transmission and in new areas in South America and the Caribbean. The virus is spread to humans by infected biting midges and some mosquito species. Although infection typically causes a self-limited febrile illness, reports of two deaths in patients with Oropouche virus infection and vertical transmission associated with adverse pregnancy outcomes have raised concerns about the threat of this virus to human health. In addition to approximately 8,000 locally acquired cases in the Americas, travel-associated Oropouche virus disease cases have recently been identified in European travelers returning from Cuba and Brazil. As of August 16, 2024, a total of 21 Oropouche virus disease cases were identified among U.S. travelers returning from Cuba. Most patients initially experienced fever, myalgia, and headache, often with other symptoms including arthralgia, diarrhea, nausea or vomiting, and rash. At least three patients had recurrent symptoms after the initial illness, a common characteristic of Oropouche virus disease. Clinicians and public health jurisdictions should be aware of the occurrence of Oropouche virus disease in U.S. travelers and request testing for suspected cases. Travelers should prevent insect bites when traveling, and pregnant persons should consider deferring travel to areas experiencing outbreaks of Oropouche virus disease.

PURPOSE: The Youth Risk Behavior Survey (YRBS) among high school students includes standard questions about sexual identity and sex of sexual contacts, but these questions are not consistently included in every state that conducts the survey. This study aimed to develop and apply a method to predict state-level proportions of high school students identifying as lesbian, gay, or bisexual (LGB) or reporting any same-sex sexual contacts in those states that did not include these questions in their 2017 YRBS. METHODS: We used state-level high school YRBS data from 2013, 2015, and 2017. We defined two primary outcomes relating to self-reported LGB identity and reported same-sex sexual contacts. We developed machine learning models to predict the two outcomes based on other YRBS variables, and comparing different modeling approaches. We used a leave-one-out cross-validation approach and report results from best-performing models. RESULTS: Modern ensemble models outperformed traditional linear models at predicting state-level proportions for the two outcomes, and we identified prediction methods that performed well across different years and prediction tasks. Predicted proportions of respondents reporting LGB identity in states that did not include direct measurement ranged between 9.4% and 12.9%. Predicted proportions of respondents reporting any same-sex contacts, where not directly observed, ranged between 7.0% and 10.4%. CONCLUSION: Comparable population estimates of sexual minority adolescents can raise awareness among state policy makers and the public about what proportion of youth may be exposed to disparate health risks and outcomes associated with sexual minority status. This information can help decision makers in public health and education agencies design, implement and evaluate community and school interventions to improve the health of LGB youth.

BACKGROUND: Pathogenic Leptospira species are globally important zoonotic pathogens capable of infecting a wide range of host species. In marine mammals, reports of Leptospira have predominantly been in pinnipeds, with isolated reports of infections in cetaceans. CASE PRESENTATION: On 28 June 2021, a 150.5 cm long female, short-beaked common dolphin (Delphinus delphis delphis) stranded alive on the coast of southern California and subsequently died. Gross necropsy revealed multifocal cortical pallor within the reniculi of the kidney, and lymphoplasmacytic tubulointerstitial nephritis was observed histologically. Immunohistochemistry confirmed Leptospira infection, and PCR followed by lfb1 gene amplicon sequencing suggested that the infecting organism was L.kirschneri. Leptospira DNA capture and enrichment allowed for whole-genome sequencing to be conducted. Phylogenetic analyses confirmed the causative agent was a previously undescribed, divergent lineage of L.kirschneri. CONCLUSIONS: We report the first detection of pathogenic Leptospira in a short-beaked common dolphin, and the first detection in any cetacean in the northeastern Pacific Ocean. Renal lesions were consistent with leptospirosis in other host species, including marine mammals, and were the most significant lesions detected overall, suggesting leptospirosis as the likely cause of death. We identified the cause of the infection as L.kirschneri, a species detected only once before in a marine mammal - a northern elephant seal (Mirounga angustirostris) of the northeastern Pacific. These findings raise questions about the mechanism of transmission, given the obligate marine lifestyle of cetaceans (in contrast to pinnipeds, which spend time on land) and the commonly accepted view that Leptospira are quickly killed by salt water. They also raise important questions regarding the source of infection, and whether it arose from transmission among marine mammals or from terrestrial-to-marine spillover. Moving forward, surveillance and sampling must be expanded to better understand the extent to which Leptospira infections occur in the marine ecosystem and possible epidemiological linkages between and among marine and terrestrial host species. Generating Leptospira genomes from different host species will yield crucial information about possible transmission links, and our study highlights the power of new techniques such as DNA enrichment to illuminate the complex ecology of this important zoonotic pathogen.

As antiretroviral treatment (ART) coverage for people living with HIV (PLHIV) increases, HIV programmes require up-to-date information about evolving HIV risk behaviour and transmission risk, including those with low-level viremia (LLV; >50 to ≤1000 copies/mL), to guide prevention priorities. We aimed to assess differences in sexual risk behaviours, distribution of viral load (VL) and proportion of transmission across PLHIV subgroups. We analysed data from Population-based HIV Impact Assessment surveys in 14 sub-Saharan African countries during 2015-2019. We estimated adjusted prevalence ratios (aPR) of self-reported HIV high-risk behaviour (multiple partners and condomless sex) across cascade stages via generalised estimation equations. We modelled the proportions of transmission from each subgroup using relative self-reported sexual risk, a Hill function for transmission rate by VL, and proportions within cascade stages from surveys and UNAIDS country estimates for 2010-2020. Compared to PLHIV with undetectable VL (≤50 copies/mL), undiagnosed PLHIV (aPR women: 1.28 [95% CI: 1.08-1.52]; men: 1.61 [1.33-1.95]) and men diagnosed but untreated (2.06 [1.52-2.78]) were more likely to self-report high-risk sex. High-risk behaviour was not significantly associated with LLV. Mean VL was similar among undiagnosed, diagnosed but untreated, and on ART but non-suppressed sub-groups. Across surveys, undiagnosed and diagnosed but untreated contributed most to transmission (40-91% and 1-41%, respectively), with less than 1% from those with LLV. Between 2010 and 2020, the proportion of transmission from individuals on ART but non-suppressed increased. In settings with high ART coverage, effective HIV testing, ART linkage, and retention remain priorities to reduce HIV transmission. Persons with LLV are an increasing share of PLHIV but their contribution to HIV transmission was small. Improving suppression among PLHIV on ART with VL ≥1000 copies/mL will become increasingly important.

INTRODUCTION: to support polio eradication activities in Nigeria, in 2012 the National Polio Emergency Operation Center (NEOC) created the Management Support Teams (MST) to address gaps in the quality of supervision of polio vaccination teams. The National Stop Transmission of Polio (NSTOP) Program supported the polio eradication activities by deploying trained supervisors as part of the MST for polio and non-polio immunization campaigns. METHODS: trained MST members were deployed approximately 4 days before the start of the campaign to participate in pre-implementation activities and supervise vaccination teams during campaigns. Terms of reference (TOR) developed by NEOC was provided to MST members to guide their activities. Qualified MSTs that met pre-determined criteria were selected and deployed to the field to support pre, intra and post campaigns activities. RESULTS: a pool of over 400 MST personnel have been identified, trained, and repeatedly deployed from 2012 till 2016. The number of deployed MST personnel rose from 40 per campaign in October 2012 to 342 in May 2016. Of these, 270 (79%) MST personnel were deployed to 11 polio high-risk states of northern Nigeria, where campaigns are conducted between eight and ten times yearly as planned by NEOC. For measles campaigns, about 300 (75%) MST personnel were deployed for the one-off northern and southern campaigns in 2016. The results of clustered Lot Quality Assurance Sampling (LQAS) post-campaign vaccination coverage surveys, a measure of campaign quality, of which introduction into the polio program coincided with deployment of MSTs, showed improvement over time, from 10% (very poor quality) in February 2012 to about 90% (good quality) in December 2016. CONCLUSION: the deployment of MST personnel increased the number of trained supervisors in the field, frequency of supervisory visits and had a positive impact on the quality of polio campaigns.

INTRODUCTION: In 2012, the World Health Organization revised treatment guidelines for childhood pneumonia with lower chest wall indrawing (LCWI) but no 'danger signs', to recommend home-based treatment. We analysed data from children hospitalized with LCWI pneumonia in the Pneumonia Etiology Research for Child Health (PERCH) study to identify sub-groups with high odds of mortality, who might continue to benefit from hospital management but may not be admitted by staff implementing the 2012 guidelines. We compare the proportion of deaths identified using the criteria in the 2012 guidelines, and the proportion of deaths identified using an alternative set of criteria from our model. METHODS: PERCH enrolled a cohort of 2189 HIV-negative children aged 2-59 months who were admitted to hospital with LCWI pneumonia (without obvious cyanosis, inability to feed, vomiting, convulsions, lethargy or head nodding) between 2011-2014 in Kenya, Zambia, South Africa, Mali, The Gambia, Bangladesh, and Thailand. We analysed risk factors for mortality among these cases using predictive logistic regression. Malnutrition was defined as mid-upper-arm circumference <125mm or weight-for-age z-score <-2. RESULTS: Among 2189 cases, 76 (3·6%) died. Mortality was associated with oxygen saturation <92% (aOR 3·33, 1·99-5·99), HIV negative but exposed status (4·59, 1·81-11·7), moderate or severe malnutrition (6·85, 3·22-14·6) and younger age (infants compared to children 12-59 months old, OR 2·03, 95%CI 1·05-3·93). At least one of three risk factors: hypoxaemia, HIV exposure, or malnutrition identified 807 children in this population, 40% of LCWI pneumonia cases and identified 86% of the children who died in hospital (65/76). Risk factors identified using the 2012 WHO treatment guidelines identified 66% of the children who died in hospital (n = 50/76). CONCLUSIONS: Although it focuses on treatment failure in hospital, this study supports the proposal for better risk stratification of children with LCWI pneumonia. Those who have hypoxaemia, any malnutrition or those who were born to HIV positive mothers, experience poorer outcomes than other children with LCWI pneumonia. Consistent identification of these risk factors should be prioritised and children with at least one of these risk factors should not be managed in the community.

BACKGROUND: The Democratic Republic of the Congo has had 15 Ebola virus disease (EVD) outbreaks, from 1976 to 2023. On June 1, 2020, the Democratic Republic of the Congo declared an outbreak of EVD in the western Équateur Province (11th outbreak), proximal to the 2018 Tumba and Bikoro outbreak and concurrent with an outbreak in the eastern Nord Kivu Province. In this Article, we assessed whether the 11th outbreak was genetically related to previous or concurrent EVD outbreaks and connected available epidemiological and genetic data to identify sources of possible zoonotic spillover, uncover additional unreported cases of nosocomial transmission, and provide a deeper investigation into the 11th outbreak. METHODS: We analysed epidemiological factors from the 11th EVD outbreak to identify patient characteristics, epidemiological links, and transmission modes to explore virus spread through space, time, and age groups in the Équateur Province, Democratic Republic of the Congo. Trained field investigators and health professionals recorded data on suspected, probable, and confirmed cases, including demographic characteristics, possible exposures, symptom onset and signs and symptoms, and potentially exposed contacts. We used blood samples from individuals who were live suspected cases and oral swabs from individuals who were deceased to diagnose EVD. We applied whole-genome sequencing of 87 available Ebola virus genomes (from 130 individuals with EVD between May 19 and Sept 16, 2020), phylogenetic divergence versus time, and Bayesian reconstruction of phylogenetic trees to calculate viral substitution rates and study viral evolution. We linked the available epidemiological and genetic datasets to conduct a genomic and epidemiological study of the 11th EVD outbreak. FINDINGS: Between May 19 and Sept 16, 2020, 130 EVD (119 confirmed and 11 probable) cases were reported across 13 Équateur Province health zones. The individual identified as the index case reported frequent consumption of bat meat, suggesting the outbreak started due to zoonotic spillover. Sequencing revealed two circulating Ebola virus variants associated with this outbreak-a Mbandaka variant associated with the majority (97%) of cases and a Tumba-like variant with similarity to the ninth EVD outbreak in 2018. The Tumba-like variant exhibited a reduced substitution rate, suggesting transmission from a previous survivor of EVD. INTERPRETATION: Integrating genetic and epidemiological data allowed for investigative fact-checking and verified patient-reported sources of possible zoonotic spillover. These results demonstrate that rapid genetic sequencing combined with epidemiological data can inform responders of the mechanisms of viral spread, uncover novel transmission modes, and provide a deeper understanding of the outbreak, which is ultimately needed for infection prevention and control during outbreaks. FUNDING: WHO and US Centers for Disease Control and Prevention.

BACKGROUND: Klebsiella pneumoniae is an important cause of nosocomial and community-acquired pneumonia and sepsis in children, and antibiotic-resistant K pneumoniae is a growing public health threat. We aimed to characterise child mortality associated with this pathogen in seven high-mortality settings. METHODS: We analysed Child Health and Mortality Prevention Surveillance (CHAMPS) data on the causes of deaths in children younger than 5 years and stillbirths in sites located in seven countries across sub-Saharan Africa (Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa) and south Asia (Bangladesh) from Dec 9, 2016, to Dec 31, 2021. CHAMPS sites conduct active surveillance for deaths in catchment populations and following reporting of an eligible death or stillbirth seek consent for minimally invasive tissue sampling followed by extensive aetiological testing (microbiological, molecular, and pathological); cases are reviewed by expert panels to assign immediate, intermediate, and underlying causes of death. We reported on susceptibility to antibiotics for which at least 30 isolates had been tested, and excluded data on antibiotics for which susceptibility testing is not recommended for Klebsiella spp due to lack of clinical activity (eg, penicillin and ampicillin). FINDINGS: Among 2352 child deaths with cause of death assigned, 497 (21%, 95% CI 20-23) had K pneumoniae in the causal chain of death; 100 (20%, 17-24) had K pneumoniae as the underlying cause. The frequency of K pneumoniae in the causal chain was highest in children aged 1-11 months (30%, 95% CI 26-34; 144 of 485 deaths) and 12-23 months (28%, 22-34; 63 of 225 deaths); frequency by site ranged from 6% (95% CI 3-11; 11 of 184 deaths) in Bangladesh to 52% (44-61; 71 of 136 deaths) in Ethiopia. K pneumoniae was in the causal chain for 450 (22%, 95% CI 20-24) of 2023 deaths that occurred in health facilities and 47 (14%, 11-19) of 329 deaths in the community. The most common clinical syndromes among deaths with K pneumoniae in the causal chain were sepsis (44%, 95% CI 40-49; 221 of 2352 deaths), sepsis in conjunction with pneumonia (19%, 16-23; 94 of 2352 deaths), and pneumonia (16%, 13-20; 80 of 2352 deaths). Among K pneumoniae isolates tested, 121 (84%) of 144 were resistant to ceftriaxone and 80 (75%) of 106 to gentamicin. INTERPRETATION: K pneumoniae substantially contributed to deaths in the first 2 years of life across multiple high-mortality settings, and resistance to antibiotics used for sepsis treatment was common. Improved strategies are needed to rapidly identify and appropriately treat children who might be infected with this pathogen. These data suggest a potential impact of developing and using effective K pneumoniae vaccines in reducing neonatal, infant, and child deaths globally. FUNDING: Bill & Melinda Gates Foundation.

Exposure to environmental hazards is an important determinant of health, and the frequency and severity of exposures is expected to be impacted by climate change. Through a partnership with the U.S. National Aeronautics and Space Administration, the U.S. Centers for Disease Control and Prevention's National Environmental Public Health Tracking Network is integrating timely observations and model data of priority environmental hazards into its publicly accessible Data Explorer (https://ephtracking.cdc.gov/DataExplorer/). Newly integrated data sets over the contiguous U.S. (CONUS) include: daily 5-day forecasts of air quality based on the Goddard Earth Observing System Composition Forecast, daily historical (1980-present) concentrations of speciated PM(2.5) based on the modern era retrospective analysis for research and applications, version 2, and Moderate Resolution Imaging Spectroradiometer (MODIS) daily near real-time maps of flooding (MCDWD). Data integrated into the CDC Tracking Network are broadly intended to improve community health through action by informing both research and early warning activities, including (a) describing temporal and spatial trends in disease and potential environmental exposures, (b) identifying populations most affected, (c) generating hypotheses about associations between health and environmental exposures, and (d) developing, guiding, and assessing environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors.

While the SARS-CoV-2 dynamic has been described globally, there is a lack of data from Sub-Saharan Africa. We herein report the dynamics of SARS-CoV-2 lineages from March 2020 to March 2022 in Cameroon. Of the 760 whole-genome sequences successfully generated by the national genomic surveillance network, 74% were viral sub-lineages of origin and non-variants of concern, 15% Delta, 6% Omicron, 3% Alpha and 2% Beta variants. The pandemic was driven by SARS-CoV-2 lineages of origin in wave 1 (16 weeks, 2.3% CFR), the Alpha and Beta variants in wave 2 (21 weeks, 1.6% CFR), Delta variants in wave 3 (11 weeks, 2.0% CFR), and omicron variants in wave 4 (8 weeks, 0.73% CFR), with a declining trend over time (p = 0.01208). Even though SARS-CoV-2 heterogeneity did not seemingly contribute to the breadth of transmission, the viral lineages of origin and especially the Delta variants appeared as drivers of COVID-19 severity in Cameroon.

Author summary  • Human immunodeficiency virus (HIV) drug resistance has implications for antiretroviral treatment strategies and for containing the HIV pandemic because the development of HIV drug resistance leads to the requirement for antiretroviral drugs that may be less effective, less well-tolerated, and more expensive than those used in first-line regimens.  • HIV drug resistance studies are designed to determine which HIV mutations are selected by antiretroviral drugs and, in turn, how these mutations affect antiretroviral drug susceptibility and response to future antiretroviral treatment regimens.  • Such studies collectively form a vital knowledge base essential for monitoring global HIV drug resistance trends, interpreting HIV genotypic tests, and updating HIV treatment guidelines.  • Although HIV drug resistance data are collected in many studies, such data are often not publicly shared, prompting the need to recommend best practices to encourage and standardize HIV drug resistance data sharing.  • In contrast to other viruses, sharing HIV sequences from phylogenetic studies of transmission dynamics requires additional precautions as HIV transmission is criminalized in many countries and regions.  • Our recommendations are designed to ensure that the data that contribute to HIV drug resistance knowledge will be available without undue hardship to those publishing HIV drug resistance studies and without risk to people living with HIV.

Leptospirosis, the most widespread zoonotic disease in the world, is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 266 sampled opportunistically from other California regions and species. In the five target species seroprevalences ranged from 5 to 60%, and infection prevalences ranged from 0.8 to 15.2% in all except fox squirrels (0%). Leptospira phylogenomics and patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas.

Studies of infectious disease ecology would benefit greatly from knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody-level data being one of the most promising sources of information. The use of antibody levels to back-calculate infection time requires the development of a host-pathogen system-specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. We present a way to model antibody dynamics in a Bayesian framework that facilitates the incorporation of all available information about potential infection times and apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans. Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements in infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. When applied to field data we saw reductions up to 83% in the window of possible infection times. The method substantially simplifies the challenge of modelling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology and can even be applied to cross-sectional data once the model is trained. © 2023 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

BACKGROUND: In 2021, four patients who had received solid organ transplants in the USA developed encephalitis beginning 2-6 weeks after transplantation from a common organ donor. We describe an investigation into the cause of encephalitis in these patients. METHODS: From Nov 7, 2021, to Feb 24, 2022, we conducted a public health investigation involving 15 agencies and medical centres in the USA. We tested various specimens (blood, cerebrospinal fluid, intraocular fluid, serum, and tissues) from the organ donor and recipients by serology, RT-PCR, immunohistochemistry, metagenomic next-generation sequencing, and host gene expression, and conducted a traceback of blood transfusions received by the organ donor. FINDINGS: We identified one read from yellow fever virus in cerebrospinal fluid from the recipient of a kidney using metagenomic next-generation sequencing. Recent infection with yellow fever virus was confirmed in all four organ recipients by identification of yellow fever virus RNA consistent with the 17D vaccine strain in brain tissue from one recipient and seroconversion after transplantation in three recipients. Two patients recovered and two patients had no neurological recovery and died. 3 days before organ procurement, the organ donor received a blood transfusion from a donor who had received a yellow fever vaccine 6 days before blood donation. INTERPRETATION: This investigation substantiates the use of metagenomic next-generation sequencing for the broad-based detection of rare or unexpected pathogens. Health-care workers providing vaccinations should inform patients of the need to defer blood donation for at least 2 weeks after receiving a yellow fever vaccine. Despite mitigation strategies and safety interventions, a low risk of transfusion-transmitted infections remains. FUNDING: US Centers for Disease Control and Prevention (CDC), the Biomedical Advanced Research and Development Authority, and the CDC Epidemiology and Laboratory Capacity Cooperative Agreement for Infectious Diseases.

Introduction Atherosclerosis preferentially occurs in arterial regions exposed to disturbed blood flow (d-flow), while regions exposed to stable flow (s-flow) are protected. The proatherogenic and atheroprotective effects of d-flow and s-flow are mediated in part by the global changes in endothelial cell gene expression, which regulates endothelial dysfunction, inflammation, and atherosclerosis. Previously, we identified Kallikrein-Related Peptidase 10 (KLK10, a secreted serine protease) as a flow-sensitive gene in arterial endothelial cells, but its role in endothelial biology and atherosclerosis was unknown.Methods and Results Here, we show that KLK10 is upregulated under s-flow conditions and downregulated under d-flow conditions using in vivo mouse models and in vitro studies with cultured endothelial cells (ECs). Single-cell RNA sequencing (scRNAseq) and scATAC sequencing (scATACseq) study using the partial carotid ligation mouse model showed flow-regulated KLK10 expression at the epigenomic and transcription levels. Functionally, KLK10 protected against d-flow-induced inflammation and permeability dysfunction in human artery ECs (HAECs). Further, treatment of mice in vivo with rKLK10 decreased arterial endothelial inflammation in d-flow regions. Additionally, rKLK10 injection or ultrasound-mediated transfection of KLK10-expressing plasmids inhibited atherosclerosis in ApoE-/- mice. Studies using pharmacological inhibitors and siRNAs revealed that the anti-inflammatory effects of KLK10 were mediated by a Protease Activated Receptors (PAR1/2)-dependent manner. However, unexpectedly, KLK10 did not cleave the PARs. Through a proteomics study, we identified HTRA1 (High-temperature requirement A serine peptidase 1), which bound and cleaved KLK10. Further, siRNA knockdown of HTRA1 prevented KLK10’s anti-inflammatory and barrier protective function in HAECs, suggesting that HTRA1 regulates KLK10 function. Moreover, KLK10 expression was significantly reduced in human coronary arteries with advanced atherosclerotic plaques compared to those with less severe plaques.Conclusion KLK10 is a flow-sensitive endothelial protein and, in collaboration with HTRA1, serves as an anti-inflammatory, barrier-protective, and anti-atherogenic factor.Competing Interest StatementThe authors have declared no competing interest.

While many transmission models have been developed for community spread of respiratory pathogens, less attention has been given to modeling the interdependence of disease introduction and spread seen in congregate settings, such as prisons or nursing homes. As demonstrated by the explosive outbreaks of COVID-19 seen in congregate settings, the need for effective outbreak prevention and mitigation strategies for these settings is critical. Here we consider how interventions that decrease the size of the susceptible populations, such as vaccination or depopulation, impact the expected number of infections due to outbreaks. Introduction of disease into the resident population from the community is modeled as a branching process, while spread between residents is modeled via a compartmental model. Control is modeled as a proportional decrease in both the number of susceptible residents and the reproduction number. We find that vaccination or depopulation can have a greater than linear effect on anticipated infections. For example, assuming a reproduction number of 3.0 for density-dependent COVID-19 transmission, we find that reducing the size of the susceptible population by 20% reduced overall disease burden by 47%. We highlight the California state prison system as an example for how these findings provide a quantitative framework for implementing infection control in congregate settings. Additional applications of our modeling framework include optimizing the distribution of residents into independent residential units, and comparison of preemptive versus reactive vaccination strategies.

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a bacterial infection (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve both our individual-level and population-level understanding of this host-pathogen system. We create a “host-pathogen space” by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to visualize and characterize changes in these values through time. We describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health. This mapping framework enables us to determine an individual’s location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure, including severe acute disease, chronic subclinical infection, and recovery. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and in certain cases we can impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space and using quantitative biomarker data provides more nuanced approaches for understanding and modeling disease dynamics in a system, yielding benefits for the clinician who needs to triage patients and prevent transmission, and for the disease ecologist or epidemiologist wishing to develop appropriate risk management strategies and assess health impacts on a population scale.Author Summary A pathogen can cause a range of disease severity across different host individuals, and these presentations change over the time-course from infection to recovery. These facts complicate the work of epidemiologists and disease ecologists seeking to understand the factors governing disease spread, often working with cross-sectional data. Recognizing these facts also highlights the shortcomings of classical approaches to modeling infectious disease, which typically rely on discrete and well-defined disease states. Here we show that by analyzing multiple biomarkers of health and infection simultaneously, treating these values as quantitative rather than binary indicators, and including a modest amount of longitudinal sampling of hosts, we can create a map of the host-pathogen interaction that shows the full spectrum of disease presentations and opens doors for new insights and predictions. By accounting for individual variation and capturing changes through time since infection, this mapping framework enables more robust interpretation of cross-sectional data; e.g., to detect predictive relationships between biomarkers and key outcomes such as survival, or to assess whether observed disease is associated with the pathogen of interest. This approach can help epidemiologists, ecologists and clinicians to better study and manage the many infectious diseases that exhibit complex relationships with their hosts.

Leptospirosis is the most widespread zoonotic disease in the world, yet it is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), Northern raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 260 sampled opportunistically from other regions and species. In the five target species, seroprevalences ranged from 5-60% and active infection prevalences ranged from 0.8-15.2% in all except fox squirrels (0%). Patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Importance: Although COVID-19 vaccines protect against infection and severe disease, the role of vaccination in preventing prolonged symptoms in those with subsequent infection is unclear. Objective(s): To determine differences in symptoms stratified by prior vaccination reported by healthcare personnel (HCP) 6 weeks after onset of COVID-19, and whether there were differences in timing of return to work. Design(s): Nested cohort study within a multicenter vaccine effectiveness study. HCP with COVID-19 between December 2020 and August 2021 were followed up 6 weeks after illness onset. Setting(s): Health systems in 12 U.S. states. Participant(s): HCP participating in a vaccine effectiveness study were eligible for inclusion if they had confirmed COVID-19 with either verified mRNA vaccination (symptom onset =14 days after two doses) or no prior COVID-19 vaccination. Among 681 eligible participants, 419 (61%) completed a follow-up survey approximately 6 weeks after illness onset. Exposures: Two doses of a COVID-19 mRNA vaccine compared with no COVID-19 vaccine. Main Outcomes and Measures: Presence of symptoms 6 weeks after onset of COVID-19 illness and days to return to work after COVID-19 illness. Result(s): Among 419 HCP with confirmed COVID-19, 298 (71%) reported one or more COVID-like symptoms 6 weeks after illness onset, with a lower prevalence among vaccinated participants (60.6%) compared with unvaccinated participants (60.6% vs. 79.1%; aRR 0.70, 95% CI 0.58-0.84). Vaccinated HCP returned to work a median 2.0 days (95% CI 1.0-3.0) sooner than unvaccinated HCP (aHR 1.37; 95% CI, 1.04-1.79). Conclusion(s): A history of two doses of COVID-19 mRNA vaccine among HCP with COVID-19 illness was associated with decreased risk of COVID-like symptoms at 6 weeks and earlier to return to work. Vaccination is associated with improved recovery from COVID-19, in addition to preventing symptomatic infection. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Studies of infectious disease ecology often rely heavily on knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody level data being one of the most promising sources of information. The use of antibody levels to back-calculate infection time requires the development of a host-pathogen system-specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. Here, we present a way to do this in a Bayesian framework that facilitates the incorporation of all available information about potential infection times. We apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans, leading to reductions of 51-92% in the window of possible infection times. Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements of infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. The method substantially simplifies the challenge of modeling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology, and can even be applied to cross-sectional data once the model is trained. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infection (LRTI) among infants under 6 months of age. Yet, in Kenya, little is known about healthcare workers' (HCWs) knowledge, attitudes, and perceptions around RSV disease and the prevention products under development. Between September and October 2021, we conducted a mixed methods cross-sectional survey to assess HCWs' knowledge, attitudes, and perceptions of RSV disease and RSV vaccinations in two counties. We enrolled HCWs delivering services directly at maternal and child health (MCH) departments in selected health facilities (frontline HCWs) and health management officers (HMOs). Of the 106 respondents, 94 (88.7%) were frontline HCWs, while 12 were HMOs. Two of the HMOs were members of the Kenya National Immunization Technical Advisory Group (KENITAG). Of the 104 non-KENITAG HCWs, only 41 (39.4%) had heard about RSV disease, and 38/41 (92.7%) felt that pregnant women should be vaccinated against RSV. Most participants would recommend a single-dose vaccine schedule (n = 62, 58.5%) for maximal adherence and compliance (n = 38/62, 61.3%), single dose/device vaccines (n = 50/86, 58.1%) to prevent wastage and contamination, and maternal vaccination through antenatal care clinics (n = 53, 50%). We found the need for increased knowledge about RSV disease and prevention among Kenyan HCWs.

A novel human coronavirus, now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, referred to as HCoV-19 here) that emerged in Wuhan, China in late 2019 is now causing a pandemic1. Here, we analyze the aerosol and surface stability of HCoV-19 and compare it with SARS-CoV-1, the most closely related human coronavirus.2 We evaluated the stability of HCoV-19 and SARS-CoV-1 in aerosols and on different surfaces and estimated their decay rates using a Bayesian regression model (see Supplementary Appendix). All experimental measurements are reported as mean across 3 replicates.

Considering the hazardous effects of particulate matter (PM) exposure on students and teachers and the high PM concentration issue in South Korea, air purifiers have recently been installed in most classrooms to improve air quality. However, some on-site challenges, such as operational costs and noise, have been issues with the continuous operation of air purifiers. Therefore, a guideline is needed to dynamically predict the indoor PM concentration based on the changes in outdoor PM concentration and activate the air purifiers only when necessary. This study develops a grey-box model that uses measured data and physical differential equations to perform the given objective and verifies its accuracy using ASTM D5157. Modeling and analysis results have obtained information that can form the basis for developing guidelines to address PM issues in schools: The air purifier should be operated during periods where the predicted values exceed the limit in closed windows and the air purifier is not operating. It was also confirmed that the need for the operation of the air purifier varies between schools and classrooms under the same outdoor PM concentration. Indoor PM concentration increased significantly after students' simultaneous mass movement, necessitating air purifiers' operation before and after the events. The pre-filter of the heater also aided in the removal of coarse PM. Additionally, the limitations and future development directions of the model were discussed.